Technique and methodology to address the above two settings are rather different, and we focus on the popular point-wise anomaly detection for multi-dimensional time series in this paper, which has received wide attention in literature [1]}, [2]}, [3]}. In some cases, sequence or subsequence level anomaly detection can be generalized from point level model. In addition, many practical applications require point level detection results as abnormal signal can quickly disappear in the time series especially when the sampling rate is low. We leave it for future work for generalizing our model to more macro-level anomaly detection tasks. On the other hand, as a lack of labeled anomalies necessitates the use of unsupervised approaches, we focus on unsupervised detection model for its practical applicability. This also aligns with the rising trend of adopting unsupervised learning algorithms for anomaly detection such as one-class SVM [4]}, GMM [5]}, tDCGAN [6]}, VAE [7]}, and VRNN [8]}.
